DAT 332. Matrix Analysis and Numerical Optimization. 4 Hours.
This course is an introduction to matrices and numerical optimization with applications in engineering and science. Topics include Algebra of matrices and systems of linear algebraic equations, rank, inverse, eigenvalues, eigenvectors, vector spaces, subspaces, basis, independence, orthogonal projection, determinant, linear programming and other numerical methods. Course Information: Prerequisites: MAT 115 or MAT 113 or equivalent.
DAT 444. Operations Research Methods. 4 Hours.
Quantitative methods necessary for analysis, modeling, and decision making. Topics include linear programming, transportation model, network models, decision theory, games theory, PERT-CPM, inventory models, and queueing theory. Additional topics may be chosen from integer linear programming, system simulation, and nonlinear programming. Course Information: Same as MAT 444 and PAD 431. Prerequisite: MAT 332 with grade of C or better.
DAT 502. Introduction to Statistical Computation. 4 Hours.
Explore the use of various statistical software packages, such as SAS, SPSS, and R. Topics will be selected from construction of data set, descriptive analysis, regression analysis, analysis of design experiment, multivariate analysis, categorical data analysis, discriminant analysis, cluster analysis, and presentation of data in graphic forms. Course Information: Prerequisites: CSC 225 or equivalent and MAT 121 or equivalent.
DAT 530. Advanced Statistical Methods. 4 Hours.
Topics include multiple linear regression, statistical inferences for regression model, diagnostics and remedies for multicollinearity, outlier and influential cases, model selection, logistic regression, multivariate analysis, categorical data analysis, discriminant analysis, cluster analysis. Course Information: Prerequisites: MAT 121 or equivalent.
DAT 532. Introduction to Machine Learning. 4 Hours.
Machine learning explores the design and the study of algorithms that can learn from data or experience, improve their performance, and make predictions. The course provides an overview of many concepts, techniques, and algorithms in machine learning, including supervised learning, unsupervised learning, reinforcement learning, and neural networks. Course Information: Prerequisites: CSC 385.
DAT 533. Data Mining. 4 Hours.
This course teaches advanced techniques for discovering hidden patterns in the rapidly growing data generated by businesses, science, web, and other sources. Focus is on the key tasks of data mining, including data preparation, classification, clustering, association rule mining, and evaluation. Course Information: Course is restricted to MS CSC majors and MS DAT majors only. Prerequisites: CSC 385.
DAT 534. Big Data Analytics. 4 Hours.
This course teaches concepts and techniques in managing and analyzing large data sets. Focus is on big data management, storage solutions, query processing, analytics, and big data applications. Topics include: introduction to Hadoop and YARN, MapReduce, Apache Spark, Big Data Warehousing with Hive and Spark SQL, large scale recommender systems and Large Scale Clustering and Classification. Course Information: Prerequisites: CSC 385, CSC 472, CSC 532 (co-requisite).
DAT 554. Data Analytics Capstone. 4 Hours.
This is a practicum course that allows students to apply the appropriate methods and tools for data analysis in a real-world organizational setting. The capstone course provides the opportunity to exercise different techniques for data storage, preprocessing, integration and analysis covered throughout the Master of Data Analytics curriculum in order to address challenges from different areas. Course Information: Co-requisites: CSC 534 and CSC 535.
DAT 555. Data Analytics Capstone Continuing Enrollment. 0 Hours.
This course is required for DAT students who took but have not completed Capstone course DAT 554. Students must register for DAT 555 for zero credit hour (one billable hour) in all subsequent semesters until DAT 554 is completed. Course Information: Restricted to DAT majors.
DAT 570. Advanced Topics in Data Analytics. 4 Hours.
Topics and prerequisites vary. Students may refer to the course schedule for topics and prerequisites. Restricted to Graduate Students, Data Analytics majors or Computer Science majors.